MegaTrans – human transporter machine learning models
MegaTrans — 人类运输机机器学习模型
基本信息
- 批准号:10546264
- 负责人:
- 金额:$ 86.48万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-04-01 至 2024-08-31
- 项目状态:已结题
- 来源:
- 关键词:3-DimensionalAgrochemicalsAlgorithmsAngiotensin-Converting Enzyme InhibitorsAntiviral AgentsArizonaBayesian MethodBayesian learningBehaviorBiological AssayBlood-Testis BarrierCOVID-19 treatmentCRISPR/Cas technologyChemistryClientClinicalCodeCollaborationsCollectionComputer ModelsComputer softwareConsultDataData SetDatabasesDecision TreesDescriptorDockingDrug DesignDrug IndustryDrug InteractionsDrug ModelingsEvaluationFamilyFee-for-Service PlansFingerprintFoundationsGraphHela CellsHepatocyteHumanIn VitroIndustryInternationalIntuitionInvestmentsLearningLibrariesLicensingLigandsLiteratureMachine LearningMediatingMethodsModelingMolecularNatural ProductsNucleoside TransporterOnline SystemsOrganOutputPharmaceutical PreparationsPharmacologic SubstancePhaseProcessPropertyPubChemPublic DomainsPythonsReceiver Operating CharacteristicsReportingResourcesRiskSeminal fluidSiteSoftware ToolsStructureStructure-Activity RelationshipSystemTestingToxic Environmental SubstancesToxic effectTrainingTreesUniversitiesUridineValidationVendorVirusVisualizationWorkXenobioticsbaseclinically relevantcomputerized toolsconsumer productdata curationdeep learningdesigndrug candidatedrug discoverydrug dispositionhigh throughput screeningimprovedin vitro testingin vivoinhibitorinhibitor therapyinterestlong short term memorymachine learning algorithmmachine learning methodmachine learning modelmembermodel buildingmolecular shapemolnupiravirneural networknovel therapeuticspharmacophorepredictive modelingprospectiveprototyperandom forestremdesivirside effectsoftware developmenttooltool developmentuptakeweb app
项目摘要
Summary
Being able to predict interactions with important human transporters would be of value to new drug design to
avoid compounds that interact with them and cause undesirable side effects. Conversely, some drug transporters
can be used for targeting molecules to specific organs and this may have considerable utility. Understanding the
interactions of novel drugs, natural products and environmental toxicants and their interactions with an array of
such transporters is, therefore, important for several industries, as well as from a regulatory perspective (e.g.
FDA, EPA and EMA). Being able to predict such interactions in a fast and reliable manner effectively requires
using computational approaches and learning from in vitro data, the latter a resource that is rapidly growing.
Over the past 20 years, we have been at the forefront of applying different machine learning approaches to
modeling drug transporters and, in many cases, developing datasets for transporters for which there was scant
available data. We now propose doing this for several transporters that may be important for drug discovery. In
Phase I we focused on OATP1B1 (SLCO1B1), which is an uptake transporter largely restricted to the sinusoidal
aspect of hepatocytes where it mediates transport of a variety of structurally unrelated compounds, including
members of several clinically important drug families (incl. statins, sartans and angiotensin converting enzyme
(ACE) inhibitors). We tested 476 drugs against one substrate in vitro. We then curated these data and built
machine learning models using multiple machine learning methods as well as model evaluation metrics. This
enabled us to develop models for integration in a web-based software tool called MegaTrans® that enables the
user to input their own compound structures and generate predictions for interactions with transporter/s of
interest, as well as visualize the similarity to the training set of each model using several different visualization
methods. In addition, during Phase I we also performed preliminary data curation, model building and validation
for two equilibrative nucleoside transporters (ENTs), ENT1 and ENT2, that are present at the blood testes barrier
(BTB), where they can facilitate drug disposition (e.g. for antivirals, thereby potentially eliminating a sanctuary
site for viruses detectable in semen). We generated Bayesian and pharmacophore models and used these to
predict numerous compounds that were then tested in vitro against ENTs. We used these ENT models to predict
(i) the antivirals used in treating COVID-19, remdesivir and molnupiravir, inhibit ENT activity, and that (ii)
remdesivir is an ENT substrate, as well as validating these predictions. In Phase II we plan on building on the
foundation of Phase I and propose greatly expanding the ENT1 and ENT2 models through in vitro testing (at the
University of Arizona) of >2000 approved drugs, natural products, and environmental toxicants as inhibitors of
ENT transport. We will use these data to build and validate machine learning models using several algorithms,
at Collaborations Pharmaceuticals, Inc. We will also test these models using external validation with additional
molecules from vendor libraries and drug collections that are not in the model. In this process we will also build
out the capabilities of MegaTransÒ to use 3D pharmacophore descriptors to incorporate molecular shape
features and allow 3D searches. The return on investment of such a commercial tool would be that it could assist
in the design and selection of more favorable compounds by avoiding transporters of interest (or, conversely,
allow the targeting of specific transporters to increase uptake into organs). It could also identify compounds that
are already approved that might present a drug-interaction risk. Predicting such behavior seen in vivo is ideal
and will lead to the prioritization of compounds to test in vitro for potential drug-drug interactions. In summary,
we propose generating large training sets for ENT1 and ENT2 transporters that we will use to generate an array
of validated machine learning models of interest to drug discovery (with specific interest for those generating
antivirals). MegaTransÒ will be a commercial product available for licensing by pharmaceutical, consumer
product, agrochemical and regulatory groups, as well as fee-for-service consulting provided by Collaborations
Pharmaceuticals, Inc.
总结
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Nathan J Cherrington其他文献
Nathan J Cherrington的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Nathan J Cherrington', 18)}}的其他基金
相似海外基金
Development of Natural Product-Inspired Ubiquinone Mimics as Next Generation Agrochemicals
开发受天然产物启发的泛醌模拟物作为下一代农用化学品
- 批准号:
2311665 - 财政年份:2023
- 资助金额:
$ 86.48万 - 项目类别:
Standard Grant
I-Corps: Solid phase matrix for the formulation of pest control agrochemicals
I-Corps:用于配制害虫防治农用化学品的固相基质
- 批准号:
2321053 - 财政年份:2023
- 资助金额:
$ 86.48万 - 项目类别:
Standard Grant
Accelerated Synthesis of Agrochemicals and Pharmaceuticals (ASAP): Bridging the Gap Between Research and Application
农用化学品和药物的加速合成 (ASAP):弥合研究与应用之间的差距
- 批准号:
MR/V022067/1 - 财政年份:2022
- 资助金额:
$ 86.48万 - 项目类别:
Fellowship
Total Synthesis of Bioactive Indole Alkaloids and Application as Agrochemicals
生物活性吲哚生物碱的全合成及其农药应用
- 批准号:
10462970 - 财政年份:2022
- 资助金额:
$ 86.48万 - 项目类别:
Total Synthesis of Bioactive Indole Alkaloids and Application as Agrochemicals
生物活性吲哚生物碱的全合成及其农药应用
- 批准号:
10620698 - 财政年份:2022
- 资助金额:
$ 86.48万 - 项目类别:
Electrons, leaves and light: investigating surface photodegradation of agrochemicals
电子、叶子和光:研究农用化学品的表面光降解
- 批准号:
2597114 - 财政年份:2021
- 资助金额:
$ 86.48万 - 项目类别:
Studentship
Supercritical Fluid Chromatography-Mass Spectrometry (SFC-MS) Analysis of Agrochemicals and Formulated Products
农用化学品和配方产品的超临界流体色谱-质谱 (SFC-MS) 分析
- 批准号:
2446948 - 财政年份:2020
- 资助金额:
$ 86.48万 - 项目类别:
Studentship
Development of Natural Product-Inspired Ubiquinone Mimics as Next Generation Agrochemicals
开发受天然产物启发的泛醌模拟物作为下一代农用化学品
- 批准号:
2003692 - 财政年份:2020
- 资助金额:
$ 86.48万 - 项目类别:
Standard Grant
Development of polymeric nanocarriers in continuous flow for the controlled release of agrochemicals
开发连续流动的聚合物纳米载体以控制农用化学品的释放
- 批准号:
2342112 - 财政年份:2019
- 资助金额:
$ 86.48万 - 项目类别:
Studentship
Design of interfaces for capturing pharmaceuticals, agrochemicals and dyes and their application to sustainable water protection with low environmental impact
捕获药物、农用化学品和染料的界面设计及其在低环境影响的可持续水保护中的应用
- 批准号:
19J20799 - 财政年份:2019
- 资助金额:
$ 86.48万 - 项目类别:
Grant-in-Aid for JSPS Fellows